Abstract
Non-verbal voice expressions (NVVEs) have been adopted
as a means of human-computer interaction in research studies.
However, exploring non-verbal voice-based interactions
has been constrained by the limited availability of suitable training
data and computational methods for classifying such expressions,
leading to a focus on simple binary inputs. We address
this issue with a new dataset containing 950 audio samples
comprising 6 classes of voice expressions. The data were
collected from 42 speakers who donated voice recordings. The
classifier was trained on the data using features derived from
mel-spectrograms. Furthermore, we studied the effectiveness
of data augmentation and improved over the baseline model accuracy
significantly with a test accuracy of 96.6% in a 5-fold
cross-validation. We have made CNVVE publicly accessible in
the hope that it will serve as a benchmark for future research.
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